Medical imaging methods, e.g. ultrasound (US), X-ray techniques, particularly Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or nuclear medical imaging methods such as Positron Emission Tomography (PET) allow for achieving three-dimensional image data sets, also called volumetric data, of the body of human beings as well as animals. This results in the problem how to represent these three-dimensional (3D) data, as typical display devices such as a screen and a printer provide two-dimensional (2D) image planes only. One possibility for representing a three-dimensional (3D) data record is to put a sectional plane through the data record and to represent only those image elements (voxels) of the 3D image data set situated on this sectional plane. By moving the sectional plane or by generating many sectional planes, the medical practitioner is able to visualize a three-dimensional image of the represented object.
Other methods also allow for representing the entire 3D image data set simultaneously. For example, it is possible to generate a 2D image of a three-dimensional ultrasonic image of a fetus which shows the fetus in a digitally generated top view, as shown in FIG. 11, for example. In order to generate such a 2D image, an imaging software extracts from a 3D image data set boundary surfaces between the fetus and the amniotic liquor surrounding the fetus and adds lighting effects and shadows according to a virtual light source. Such a method is called “surface rendering”.
It is also possible to represent the entire image data set in a 2D image, when image data sets have boundary surfaces which are not so well defined. In this case, the single voxels of the 3D image volume are classified according to their optical characteristics, for example as transparent or opaque, and then a 2D image is generated from a certain viewing direction, which corresponds to a certain view of the 3D image data set. Such a method is generally called “volume rendering”. FIG. 8 shows an example for a 2D image of a 3D ultrasonic image data set of the heart generated by volume rendering.
In German, rendering methods are also called “digitale Bildsynthese” (“digital image synthesis”). The term “rendering” generally relates to a method for generating a 2D image from a 3D description. This may be a 3D image data set, but also a geometric description such as a grid model, an analytic/parametric description such as formulas or algorithms, for example fractals.
In general, and also in this document, “volume rendering” refers to methods for generating a 2D image from a 3D image data set. Preferably, this image gives a certain sense of depth of the 3D image. Surface rendering is a specific variant of volume rendering.
When representing the 2D images generated by rendering, a color representation is often preferred. A color is assigned to each voxel of the 3D image volume or to each pixel of the 2D image, which is determined by means of a voxel value or pixel value listed in a color chart.
While local characteristics (for example surface texture, curvature, etc.) can be represented in a relatively good and efficient way by means of local virtual lighting (“gradient-lighting”), as shown in FIGS. 11 and 8, the sense of depth is lost in many cases. Especially when representing volume records of complicated anatomic regions (for example 3D/4D ultrasonic images), it is often difficult to understand the global positioning.
In the state of the art, methods for improving the sense of depth of 3D image data sets are generally called “depth cueing”. Depth cueing refers to a group of effects, which change certain material characteristics or lighting characteristics as a function of the depth relative to the observer in order to achieve a better sense of depth. The so-called “fogging” for instance is commonly applied to make the color of an object fading for example to white or black towards the background. Another method is described in D. Weiskopf, T. Ertl: “Real-Time Depth-Cueing beyond Fogging”, Journal of Graphics Tools, Vol. 7, No. 4, 2002. This article proposes to change the color saturation in such a way that in the foreground of the image full color saturation is used, while in the background of the image only shades of grey are used.
The article of D. Ebert, P. Rheingans: “Volume Illustration: Non-Photorealistic Rendering of Volume Data”, IEEE Visualization 2000, 2000, proposes to combine intensity-depth cueing with a slight modification of the shade of color. For example, with increasing depth the color can fade to blue, as already practiced by Leonardo da Vinci. Such methods of changing the shade of color with increasing depth of the picture have very different effects according to the predominant color of the picture. If, for example, a color chart already containing a blue gradient was used for generating the picture, a background change to blue is not very efficient and rather irritating.
US 2006/0173326 describes a method of depth cueing of ultrasonic images in which the color of “color-flow” (Doppler) images is changed with increasing distance to the observer in order to give a sense of depth.
Another possibility to generate a sense of depth is perspective foreshortening. However, this is not applicable particularly for anatomic data, because these images are often not clearly arranged and the observer is not able to distinguish between perspective foreshortening and atrophy of the organ observed.
The methods for depth cueing have also major disadvantages: fogging makes the image unclear in greater depth, the contours become blurred “grey-in-grey”, and the sense of depth is accompanied by a loss of information.
In summary, the methods of depth cueing have great disadvantages particularly in the field of representation of medical 3D or 4D image data sets. Therefore, the global relations (relative orientation, distance, position, etc.) of the different structures in an anatomic image data set cannot be recognized easily in many cases.